Senior Machine Learning Engineer

At least 4 years experience as a Machine Learning Engineer, Software Engineer, or Data Engineer 4-year Bachelor's degree in Computer Engineering or a related field Experience deploying data science models in a production setting. Expertise in Python, Scala, Java, or another modern programming language The ability to build and operate robust data pipelines using a variety of data sources, programming languages, and toolsets Strong working knowledge of SQL and the ability to write, debug, and optimize distributed SQL queries Experience working with Data Science/Machine Learning software and libraries such as h2o, TensorFlow, Keras, scikit-learn, etc. Experience with Docker, Kubernetes, or some other containerization technology Familiarity with multiple data source systems (e.g. JMS, Kafka, RDBMS, DWH, MySQL, Oracle, SAP) Systems-level knowledge in network/cloud architecture, operating systems (e.g., Linux), storage systems (e.g., AWS, Databricks, Cloudera) Production experience in core data technologies (e.g. Spark, Pandas) Development of APIs and web server applications (e.g. Flask, Django, Spring) Complete software development lifecycle experience including design, documentation, ong analytical abilities; ability to translate business requirements and use cases into a solution, including ingestion of many data sources, ETL processing, data access, and consumption, as well as custom analytics Excellent communication and presentation skills; previous experience working with internal or external customers

Design and create environments for data scientists to build models and manipulate data Work within customer systems to extract data and place it within an analytical environment Learn and understand customer technology environments and systems Define the deployment approach and infrastructure for models and be responsible for ensuring that businesses can use the models we develop Reveal the true value of data by working with data scientists to manipulate and transform data into appropriate formats in order to deploy actionable machine learning models Partner with data scientists to ensure solution deployability—at scale, in harmony with existing business systems and pipelines, and such that the solution can be maintained throughout its life cycle Create operational testing strategies, validate and test the model in QA, and implementation, testing, and deployment Ensure the quality of the delivered product